A preliminary study of sperm identification in microdissection testicular sperm extraction samples with deep convolutional neural networks / 亚洲男科学杂志(英文版)

Sperm identification and selection is an essential task when processing human testicular samples for in vitro fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.

Hypertension and Male Fertility

As the age of paternity rises in the developed world, issues of chronic disease may affect prospective fathers. Given the high prevalence of hypertension, researchers have begun to explore the relationship between hypertensive disease and male fertility. The current literature suggests an association between hypertension and semen quality. The use of various antihypertensive medications has also been linked to impaired semen parameters, making it difficult to discern whether the association exists with hypertension or its treatment. Further investigation is warranted to determine whether the observed associations are causal.